Linear regression is perhaps one of the most well known and well-understood algorithms in statistics and machine learning. In this post, you will discover the linear regression algorithm, how it works, and how you can best use it in on your machine learning projects. Contents… •Linear Regression •Simple Linear Regression •Multiple Linear Regression •Assumptions •Applications L inear Regression A statistical approach for modeling relationship between a dependent variable with a given set of independent variables. We refer dependent variables as response and independent variables as features Simple Linear Regression Simple linear regression is an approach for predicting a response using a single feature. It is assumed that the two variables are linearly related. Hence, we try to find a linear function that predicts the response value(y) as accurately as possible as a function of the feature or independent variable(x). Let us consider a dataset where we have a value of response y for
Not really next-gen JavaScript, but also important: JavaScript array functions like map() , filter() , reduce() etc. You’ll see me use them quite a bit since a lot of React concepts rely on working with arrays (in immutable ways). The following page gives a good overview of the various methods you can use on the array prototype — feel free to click through them and refresh your knowledge as required: https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/Array map() => https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/Array/map find() => https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/Array/find findIndex() => https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/Array/findIndex filter() => https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/Array/filter reduce() => https://developer.mozilla.org/en-US/d